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In quantum computing, the variational quantum eigensolver (VQE) is a quantum algorithm for quantum chemistry, quantum simulations and optimization problems. It is a hybrid algorithm that uses both classical computers and quantum computers to find the ground state of a given physical system. Given a guess or ansatz, the quantum processor calculates the expectation value of the system with respect to an observable, often the Hamiltonian, and a classical optimizer is used to improve the guess. The algorithm is based on the variational method of quantum mechanics.

It was originally proposed in 2014, with corresponding authors Alberto Peruzzo, Alán Aspuru-Guzik and Jeremy O'Brien.[a][1][2] The algorithm has also found applications in quantum machine learning and has been further substantiated by general hybrid algorithms between quantum and classical computers.[3] It is an example of a noisy intermediate-scale quantum (NISQ) algorithm.

Description

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Pauli encoding

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The objective of the VQE is to find a set of quantum operations that prepares the lowest energy state (or minima) of a close approximation to some target quantity or observable. While the only strict requirement for the representation of an observable is its efficiency in estimating its expectation values, it is often more straightforward if the operator has a compact or simple expression in terms of Pauli operators or tensor products of Pauli operators.

For a fermionic system, it is often most convenient to qubitize: that is to write the many-body Hamiltonian of the system using second quantization, and then use a mapping to write the creation-annihilation operators in terms of Pauli operators. Common schemes for fermions include Jordan–Wigner transformation, Bravyi–Kitaev transformation, and parity transformation.[4][5]

Once the Hamiltonian is written in terms of Pauli operators and irrelevant states are discarded (finite-dimensional space), it would consist of a linear combination of Pauli strings consisting of tensor products of Pauli operators (for example ), such that

,

where are numerical coefficients. Based on the coefficients, the number of Pauli strings can be reduced in order to optimize the calculation.[6]

The VQE can be adapted to other optimization problems by adapting the Hamiltonian to be a cost function.[7]

Ansatz and initial trial function

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The choice of ansatz state depends on the system of interest. In gate-based quantum computing, the ansatz is given by a parametrized quantum circuit, whose parameters can be updated after each run. The ansatz has to be adaptable enough to not miss the desired state. A common method to obtain a valid ansatz is given by the unitary coupled cluster (UCC) framework and its extensions.[5]

If the ansatz is not chosen adequately the procedure may halt at suboptimal parameters that do not correspond to a minima. In this situation, the algorithm is said to have reached a 'barren plateau'.[5]

Example of a hardware efficient ansatz.

The ansatz can be set to an initial trial function to start the algorithm. For example, for a molecular system, one can use the Hartree–Fock method to provide a starting state that is close to the real ground state.

Another variant of the ansatz circuit is the hardware efficient ansatz, which consists of sequence of 1 qubit rotational gates and 2 qubit entangling gates.[citation needed] The number of repetitions of 1-qubit rotational gates and 2-qubit entangling gates is called the depth of the circuit.

Measurement

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The expectation value of a given state with parameters , has an expectation value of the energy or cost function given by

so in order to obtain the expectation value of the energy, one can measure the expectation value of each Pauli string (number of counts for a given value over the total number of counts). This step corresponds to measuring each qubit in the axis provided by the Pauli string.[7] For example, for the string , the first qubit is to be measured in the x-axis, while the last two are to be measured in the y-axis of the Bloch sphere. If measurement in the z-axis is only possible, then Clifford gates can be used to transform between axes. If two Pauli strings commute, then they can be both measured simultaneously using the same circuit and interpreting the result according to the Pauli algebra.

Variational method and optimization

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Given a parametrized ansatz for the ground state eigenstate, with parameters that can be modified, one is sure to find the parametrized state that is closest to the ground state based on the variational method of quantum mechanics. Using classical algorithms in a digital computer, the parameters of the ansatz can be optimized. For this minimization, it is necessary to find the minima of a multivariable function. Classical optimizers using gradient descent can be used for this purpose.[7]

Formulation

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For a given Hamiltonian (H) and a state vector if we can vary arbitrarily then will be the ground state energy and would be a ground state (assuming no degeneracy). But the above minimization problem over all possible states , where state is dimensional, is impractical. Thus to restrict the search space to a more practical size (e.g. poly(n)), we need to restrict the to only a subset of possible n-qubit states which is based on conventional physics, chemistry and quantum mechanics knowledge.

High Level illustration of Variational Quantum Algorithm

Algorithm

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The adjoining figure illustrates the high level steps in the VQE algorithm.

The circuit controls the subset of possible states that can be created, and the parameter contains the variational parameters, where the number of parameters chosen are enough to lend the algorithm expressive power to compute the ground state of the system, but not too big to increase the computational cost of the optimization step.

By running the circuit many times and constantly updating the parameters to find the global minima of the expectation value of the desired observable, one can approach the ground state of the given system and store it in a quantum processor as a series of quantum gate instructions.

In case of gradient descent, its required to minimize a cost function where for the VQE case . The update rule is:

where r is the learning rate (step size) and

In order to compute the gradients, the parameter shift rule is used.[8][9]

Example

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Considering a single Pauli gate example:

where P = X,Y or Z, then

As, . Thus,

The above result has interesting properties as:

  1. The same circuit can be used to evaluate and
  2. needs to be evaluated 2 times to arrive at the gradient value
  3. As the angle precision is large, gate precision can be kept low

Advantages and disadvantages

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  1. The VQE circuit does not require many gates compared with quantum phase estimation algorithm (QPE), it is more robust to errors and lends itself well to error mitigation strategies.
  2. It is a heuristic method and thus does not guarantee convergence to the ground state value. The method is highly influenced by the choice of ansatz circuit and the optimization methods.
  3. Number of measurements required to conclude the value of ground state is higher compared to the QPE and scales approximately with the number of terms in the Hamiltonian.
  4. VQE can run on NISQ hardware.
  5. VQE is highly versatile, as problems (apart from chemistry) can be expressed as Hamiltonians.

Use

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In chemistry

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As of 2022, the variational quantum eigensolver can only simulate small molecules like the helium hydride ion[1] or the beryllium hydride molecule.[10] Larger molecules can be simulated by taking into account symmetry considerations. In 2020, a 12-qubit simulation of a hydrogen chain (H12) was demonstrated using Google's Sycamore quantum processor.[11]

See also

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Notes

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References

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Revisions and contributorsEdit on WikipediaRead on Wikipedia

Variational quantum eigensolver

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The variational quantum eigensolver (VQE) is a hybrid quantum-classical algorithm designed to approximate the ground-state energy and wavefunction of a quantum system by variationally minimizing the expectation value of its Hamiltonian using a parameterized quantum circuit prepared on a quantum processor, combined with classical optimization techniques.[1] Introduced in 2014 on a photonic quantum processor, VQE leverages the variational principle from quantum mechanics, which guarantees that the computed energy provides an upper bound to the true ground-state energy, making it particularly suitable for noisy intermediate-scale quantum (NISQ) devices with limited coherence times compared to methods like quantum phase estimation.[1][2] Key components of VQE include the ansatz, a parameterized quantum circuit that generates trial wavefunctions (often inspired by classical methods like unitary coupled cluster for chemistry applications); the measurement strategy, which computes the expectation value of the Hamiltonian's Pauli terms on the quantum hardware; and the classical optimizer, such as gradient descent or derivative-free methods like COBYLA, which iteratively adjusts the ansatz parameters to minimize the energy.[2] This iterative process allows VQE to handle complex many-body Hamiltonians that are intractable on classical computers, with demonstrated applications in quantum chemistry for molecules like H₂ and He–H⁺, as well as in condensed matter physics for modeling Ising models and frustrated magnets.[1][2] Since its inception, VQE has evolved with advancements in error mitigation techniques, such as zero-noise extrapolation and readout error correction, to improve accuracy on current hardware, and extensions like adaptive ansatzes that dynamically build circuits to reduce circuit depth and barren plateaus in the optimization landscape.[2] Ongoing research addresses challenges including measurement overhead, noise resilience, and scalability, positioning VQE as a cornerstone for near-term quantum simulations in fields ranging from drug discovery to materials design, though quantum advantage remains contingent on mitigating large prefactors in computational cost.[2]

Introduction

Overview

The Variational Quantum Eigensolver (VQE) is a hybrid quantum-classical algorithm designed to approximate the ground-state energy and corresponding wavefunction of a quantum system by preparing variational trial states using parameterized quantum circuits and minimizing the expectation value of the system's Hamiltonian through classical optimization.[1][3] This approach leverages the variational principle, ensuring that the resulting energy serves as an upper bound to the true ground-state value, while the Hamiltonian—representing the total energy of the system—is encoded into measurable observables on the quantum hardware.[1][3] VQE has emerged as a key method in the Noisy Intermediate-Scale Quantum (NISQ) era, where quantum devices with 50–100 qubits and imperfect gates limit the feasibility of fully fault-tolerant algorithms.[3] It addresses quantum many-body problems, such as simulating molecular electronic structures for quantum chemistry applications, which scale exponentially and become intractable on classical computers for systems beyond a few atoms.[1] By requiring only short-depth circuits and tolerating noise through iterative refinement, VQE enables practical computations on current quantum hardware without the need for error correction.[3] At a high level, the algorithm proceeds by initializing parameters for a quantum ansatz circuit on the quantum processor to generate a trial state, measuring the energy expectation value via repeated executions, and feeding these results to a classical routine that adjusts the parameters to lower the energy until convergence.[1][3] This iterative hybrid loop exploits the strengths of both quantum state preparation and classical optimization, making VQE suitable for exploring ground states in condensed matter physics and beyond.[3]

History

The variational quantum eigensolver (VQE) originated in 2014 with the work of Peruzzo et al., who introduced it as a hybrid quantum-classical algorithm to approximate ground-state energies of molecular Hamiltonians using limited quantum resources.[1] Their approach leveraged the variational principle within a photonic quantum processor, enabling practical simulations despite hardware noise.[1] This marked a pivotal shift toward near-term quantum algorithms suitable for noisy intermediate-scale quantum (NISQ) devices. A key early milestone was the first experimental demonstration of VQE on the He–H⁺ molecule in the same 2014 study, where Peruzzo et al. achieved chemical accuracy for bond dissociation energies using a four-qubit setup.[1] In 2016, VQE was extended to the H₂ molecule on superconducting qubit hardware.[4] Subsequent extensions in 2017 by Kandala et al. advanced the method by introducing hardware-efficient ansatze optimized for superconducting quantum processors, alongside readout error mitigation techniques to enhance reliability on multi-qubit systems.[5] Further developments included explorations of unitary coupled-cluster ansatze, which provided chemically inspired trial wavefunctions for improved expressivity, as detailed in the comprehensive review by McArdle et al. From 2018 onward, VQE gained widespread accessibility through integration into open-source frameworks such as IBM's Qiskit (via its Aqua chemistry module) and Google's Cirq paired with OpenFermion, enabling standardized implementations and simulations across diverse hardware backends.[6] The focus on NISQ-era applications intensified after Google's 2019 Sycamore experiment demonstrated quantum advantage, prompting refinements in VQE toward hardware-efficient ansatze that minimize circuit depth and error accumulation on available processors.

Theoretical Foundations

Variational Principle

The variational theorem, a cornerstone of quantum mechanics, states that for a Hermitian Hamiltonian operator $ H $ with ground-state eigenvector $ |\psi_0\rangle $ and corresponding eigenvalue $ E_0 $, the expectation value $ \langle \psi | H | \psi \rangle $ for any normalized trial state $ |\psi\rangle $ satisfies $ \langle \psi | H | \psi \rangle \geq E_0 $, with equality holding only if $ |\psi\rangle = |\psi_0\rangle $.[7] This principle, often expressed through the Rayleigh quotient $ R(\psi) = \frac{\langle \psi | H | \psi \rangle}{\langle \psi | \psi \rangle} $, ensures that the ground-state energy minimizes the energy functional over the Hilbert space.[8] The proof relies on the Rayleigh-Ritz method, which approximates the ground state by minimizing the Rayleigh quotient over a finite-dimensional trial manifold spanned by basis functions. Consider a trial state $ |\phi(\mathbf{a})\rangle = \sum_i a_i |\phi_i\rangle $ in a subspace of dimension $ N $; the estimated energy is $ E_{\text{est}}(\mathbf{a}) = \frac{\sum_{i,j} a_i^* a_j \langle \phi_i | H | \phi_j \rangle}{\sum_{i,j} a_i^* a_j \langle \phi_i | \phi_j \rangle} $, minimized by solving the generalized eigenvalue problem $ \det(H - \lambda S) = 0 $, where $ H_{ij} = \langle \phi_i | H | \phi_j \rangle $ and $ S_{ij} = \langle \phi_i | \phi_j \rangle $.[8] The lowest eigenvalue of this matrix provides an upper bound to $ E_0 $, as the subspace projection restricts the minimization, and expanding the basis monotonically decreases the estimates toward the exact value from above.[9] In the context of quantum computing, this principle extends to parameterized trial states prepared on quantum hardware, such as variational quantum circuits $ |\psi(\boldsymbol{\theta})\rangle = U(\boldsymbol{\theta}) |\psi_{\text{init}}\rangle $, where $ U(\boldsymbol{\theta}) $ is a parameterized unitary operator and $ \boldsymbol{\theta} $ denotes tunable parameters.[7] The Rayleigh quotient is then approximated by measuring $ \langle \psi(\boldsymbol{\theta}) | H | \psi(\boldsymbol{\theta})\rangle $ on the device, enabling hybrid quantum-classical optimization to approach the ground state within hardware constraints.[8] A key implication for the variational quantum eigensolver (VQE) is its provision of rigorous upper bounds on the ground-state energy, as the variational theorem guarantees that any computed expectation value exceeds or equals $ E_0 $, offering a quantifiable measure of approximation quality without requiring full coherence over the system evolution.[7] This property distinguishes VQE from phase estimation methods and facilitates reliable error assessment in noisy intermediate-scale quantum devices.[9]

Quantum Hamiltonians and Encoding

In the context of the variational quantum eigensolver (VQE), quantum Hamiltonians relevant to applications like quantum chemistry are typically formulated in second-quantized form to describe fermionic systems, such as electrons in molecules. This representation leverages creation ($ \hat{a}^\dagger_p )andannihilation() and annihilation ( \hat{a}_q $) operators that obey anticommutation relations, efficiently capturing the many-body nature of the problem while reducing the Hilbert space dimensionality compared to first quantization. The general form of the electronic Hamiltonian is
H^=∑p,qhpqa^p†a^q+12∑p,q,r,shpqrsa^p†a^q†a^sa^r, \hat{H} = \sum_{p,q} h_{pq} \hat{a}^\dagger_p \hat{a}_q + \frac{1}{2} \sum_{p,q,r,s} h_{pqrs} \hat{a}^\dagger_p \hat{a}^\dagger_q \hat{a}_s \hat{a}_r,
where the sums run over spin-orbital indices, $ h_{pq} $ are one-electron integrals (including kinetic energy and nuclear attraction), and $ h_{pqrs} $ are two-electron repulsion integrals, ensuring antisymmetry under particle exchange.[10] This structure arises from the Born-Oppenheimer approximation and Hartree-Fock basis sets, making it central to simulating molecular ground states.[11] To execute VQE on gate-based quantum computers, which operate on qubits, the fermionic Hamiltonian must be mapped to an equivalent operator in the qubit space. This fermion-to-qubit transformation preserves the algebra of the fermionic operators while expressing them as products of Pauli matrices ($ \hat{I}, \hat{X}, \hat{Y}, \hat{Z} $). Two seminal mappings are the Jordan-Wigner and Bravyi-Kitaev transformations, both requiring one qubit per spin-orbital for an $ N $-orbital system, thus incurring a linear qubit overhead in the basis set size. The Jordan-Wigner transformation, originally proposed for spin chains but adapted for fermions in quantum chemistry simulations, encodes each fermionic mode as a qubit and represents creation operators as
a^j†=12(X^j−iY^j)∏k=0j−1Z^k, \hat{a}^\dagger_j = \frac{1}{2} (\hat{X}_j - i \hat{Y}_j) \prod_{k=0}^{j-1} \hat{Z}_k,
with a similar form for annihilation operators; this introduces long-range string operators that enforce the Pauli exclusion principle but result in Pauli terms with weights up to $ O(N) $, leading to non-local interactions.[11] In contrast, the Bravyi-Kitaev transformation uses a binary tree (or Fenwick tree) structure to balance occupation number and parity encoding, yielding more local operators with typical Pauli weights of $ O(\log N) $; for instance, the creation operator takes the form
a^j†=12Z^P(j)⊗(X^j−iY^j)⊗∏u∈U(j)X^u, \hat{a}^\dagger_j = \frac{1}{2} \hat{Z}_{P(j)} \otimes (\hat{X}_j - i \hat{Y}_j) \otimes \prod_{u \in U(j)} \hat{X}_u,
where $ P(j) $ and $ U(j) $ denote parity and update indices in the tree, reducing the range of correlations compared to Jordan-Wigner while maintaining exact equivalence.[12] Both transformations ensure the mapped Hamiltonian commutes with the total particle number and spin symmetries when appropriately tapered.[10] Following the mapping, the second-quantized Hamiltonian decomposes into a sum of Pauli strings, the native basis for qubit operators:
H^=∑kckP^k, \hat{H} = \sum_k c_k \hat{P}_k,
where each $ \hat{P}k $ is a tensor product of single-qubit Pauli operators across the $ N $ qubits, and $ c_k $ are real coefficients derived from the integrals $ h{pq} $ and $ h_{pqrs} $. This Pauli encoding facilitates the VQE workflow by allowing the expectation value $ \langle \hat{H} \rangle $ to be computed variationally, though the number of terms can grow as $ O(N^4) $ for the two-electron part before sparsity exploitation.[10] Jordan-Wigner tends to produce denser expansions with more high-weight terms, exacerbating circuit depth and error accumulation, whereas Bravyi-Kitaev yields sparser, lower-weight decompositions that better suit near-term hardware with limited connectivity.[10] A key challenge in these encodings is the qubit overhead for realistic molecules: for example, simulating heavy elements like iron in biomolecules requires basis sets with hundreds of spin-orbitals, demanding correspondingly many qubits and exceeding current noisy intermediate-scale quantum devices limited to tens of qubits.[11] Additionally, the choice between sparse (e.g., Bravyi-Kitaev, with fewer long-range terms) and denser (e.g., Jordan-Wigner) encodings trades off implementation simplicity against gate efficiency and noise resilience, with sparse variants reducing the overall resource demands but complicating circuit compilation on specific architectures.[10] These mappings thus form the foundational step in preparing chemically relevant problems for VQE, directly influencing algorithmic scalability.[12]

Algorithm Components

Ansatz Design

In the variational quantum eigensolver (VQE) algorithm, the ansatz serves as a parameterized quantum circuit that generates a trial wavefunction to approximate the ground state of a target Hamiltonian. This trial state is expressed as $ |\psi(\boldsymbol{\theta})\rangle = U(\boldsymbol{\theta}) |0\rangle $, where $ U(\boldsymbol{\theta}) $ is a unitary operator depending on variational parameters $ \boldsymbol{\theta} $, and $ |0\rangle $ is typically an initial reference state such as the all-zero computational basis state.[1] The ansatz's role is to provide a flexible, ansatz-dependent manifold of states over which the variational principle minimizes the energy expectation value, enabling the identification of low-energy eigenstates on noisy intermediate-scale quantum devices.[13] Ansatzes are broadly classified into hardware-efficient and problem-inspired types, each tailored to balance computational feasibility and representational power. Hardware-efficient ansatzes prioritize compatibility with current quantum hardware by employing shallow circuits composed of alternating layers of single-qubit rotation gates (e.g., $ R_x(\theta_i) $ and $ R_z(\theta_i) $) and native entangling two-qubit gates (e.g., CNOT or CZ), often arranged in a brickwork pattern to minimize gate depth and mitigate noise.[14] For example, in the PennyLane quantum software library, a hardware-efficient ansatz for a 2-qubit VQE can be implemented with num_layers=2; for each layer, apply RY(params[layer, wire]) to each wire, then CZ on wires [0,1]. This matches the two-local style with single-qubit rotations and linear entanglement.[15] This design has demonstrated effectiveness in simulating small molecular Hamiltonians, such as $ \ce{H2} $ and $ \ce{LiH} $, achieving chemical accuracy with reduced circuit overhead on superconducting qubit platforms.[14] In contrast, problem-inspired ansatzes incorporate domain-specific structure, such as the unitary coupled-cluster singles and doubles (UCCSD) form, which emulates classical coupled-cluster theory by exponentiating anti-Hermitian operators corresponding to fermionic single and double excitations mapped to Pauli strings via Jordan-Wigner or Bravyi-Kitaev transformations.[16] The UCCSD ansatz, $ U(\boldsymbol{\theta}) = e^{T(\boldsymbol{\theta}) - T^\dagger(\boldsymbol{\theta})} $, where $ T $ includes excitation amplitudes, excels in quantum chemistry applications by capturing electron correlation effects with high fidelity.[16] Initial trial functions often begin with a physically motivated reference state to accelerate convergence and improve accuracy. In quantum chemistry contexts, the Hartree-Fock (HF) state—representing a mean-field approximation of the molecular orbital configuration—is commonly used as the starting point, prepared by applying a sequence of gates to encode the occupied orbitals into the qubit register.[13] Adaptive ansatzes, such as the adaptive derivative-assembled pseudo-Trotter (ADAPT) VQE, extend this by incrementally constructing the circuit: operators are selected from a predefined pool (e.g., fermionic excitations) based on their gradient magnitude with respect to the energy, building a compact ansatz layer-by-layer to enhance qubit efficiency and reduce depth for molecules like $ \ce{LiH} $.[17] This approach has shown reductions to fewer than 50% of the parameters of fixed UCCSD forms while maintaining chemical accuracy.[17] Key considerations in ansatz design revolve around the trade-off between expressivity—the ansatz's capacity to span a diverse set of quantum states approaching the true ground state—and trainability, which ensures reliable optimization without encountering barren plateaus where gradients vanish exponentially.[18] Highly expressive ansatzes, such as deep hardware-efficient circuits, risk trainability issues due to concentration of the energy landscape around its mean, leading to exponentially small gradients as system size increases; this phenomenon, analyzed in two-layer circuits, underscores the need for shallower, structured designs.[19] To mitigate barren plateaus, practitioners favor ansatzes with limited depth (e.g., 1-2 layers) or symmetry-preserving elements, ensuring the variational landscape remains navigable for problems up to 20 qubits.[19]

Measurement Protocol

In the variational quantum eigensolver (VQE), the measurement protocol involves estimating the expectation value of the encoded Hamiltonian $ H = \sum_k c_k P_k $, where $ c_k $ are coefficients and $ P_k $ are Pauli strings, to evaluate the energy of a trial state $ |\psi\rangle $. This is achieved by computing $ \langle H \rangle = \sum_k c_k \langle P_k \rangle $, with each $ \langle P_k \rangle $ obtained through projective measurements on the quantum hardware. For a given Pauli string $ P_k $, the measurement basis is rotated via single-qubit gates to align with the eigenbasis of $ P_k $, after which the circuit is executed multiple times (shots) to sample measurement outcomes; the expectation value is then estimated from the frequency of +1 and -1 eigenvalues.[1] To mitigate the high circuit depth and measurement overhead associated with measuring each $ P_k $ separately, Pauli terms are grouped into sets of mutually commuting operators, allowing simultaneous estimation in a single measurement basis per group. This reduces the number of required quantum circuits from the total number of Pauli terms (often scaling exponentially with system size) to the number of such commuting groups, which can be found using graph coloring algorithms on the commutation graph of the terms. A prominent example is tapered measurements, which exploit Abelian symmetries (e.g., particle number or spin conservation) in the Hamiltonian to eliminate redundant qubits and Pauli terms while preserving the spectrum, further lowering the measurement cost. For instance, in fermionic systems encoded via Jordan-Wigner or Bravyi-Kitaev transformations, up to $ 2^s $ terms can be tapered off, where $ s $ is the number of independent symmetries.[20] Measurement errors arise primarily from shot noise, which introduces statistical variance inversely proportional to the number of shots per term, and readout errors, where misclassification of qubit states biases the estimates. These can be mitigated using zero-noise extrapolation (ZNE), which amplifies noise artificially (e.g., by inserting idle gates or twirling) and extrapolates the observable to the zero-noise limit via polynomial fitting, improving accuracy without additional hardware assumptions. Symmetry verification techniques complement this by post-selecting on measurement outcomes that respect the system's symmetries, discarding erroneous data and reducing bias from decoherence. The measurement overhead remains a bottleneck, as the number of circuits scales linearly with the number of Pauli groups (typically $ O(N^4) $ for $ N $-orbital quantum chemistry Hamiltonians before grouping), requiring thousands to millions of shots for precision. Optimizations like quantum stochastic drift (qDRIFT) address this by probabilistically sampling Pauli terms with probabilities proportional to $ |c_k| $, effectively estimating $ \langle H \rangle $ with fewer circuits at the cost of increased shot variance, which scales favorably for sparse or weakly correlated terms. This sampling approach can reduce the effective measurement cost by factors of 10–100 in practice for molecular simulations.

Classical Optimization

The classical optimization component of the variational quantum eigensolver (VQE) aims to minimize the variational energy expectation value E(θ)=⟨ψ(θ)∣H^∣ψ(θ)⟩E(\boldsymbol{\theta}) = \langle \psi(\boldsymbol{\theta}) | \hat{H} | \psi(\boldsymbol{\theta}) \rangle, where θ\boldsymbol{\theta} denotes the parameters of the quantum ansatz ∣ψ(θ)⟩=U(θ)∣0⟩|\psi(\boldsymbol{\theta})\rangle = U(\boldsymbol{\theta}) |0\rangle and H^\hat{H} is the target Hamiltonian. This minimization leverages the variational principle to approximate the ground-state energy, with the classical routine iteratively updating θ\boldsymbol{\theta} based on energy evaluations obtained from quantum measurements.[10] The process integrates both gradient-based and derivative-free optimization algorithms, selected based on factors such as noise tolerance, computational cost, and the dimensionality of θ\boldsymbol{\theta}. For instance, gradient-based methods like Adam, which employs adaptive moment estimates with hyperparameters such as learning rates and momentum coefficients, or L-BFGS, a quasi-Newton approach approximating the Hessian for efficient updates in low-dimensional spaces, require analytical gradients of E(θ)E(\boldsymbol{\theta}).[10] In contrast, derivative-free methods such as COBYLA, a constrained optimization by linear approximations algorithm that uses simplex-based searches, or Nelder-Mead, which performs direct landscape sampling without derivatives, are particularly robust in noisy quantum environments where gradient estimation may be unreliable.[10][1] A cornerstone for gradient-based optimization in VQE is the parameter-shift rule, which enables exact computation of partial derivatives ∂E/∂θk\partial E / \partial \theta_k through additional quantum circuit evaluations. For Pauli rotation gates (e.g., Rx(θ)R_x(\theta), Ry(θ)R_y(\theta), Rz(θ)R_z(\theta)) with standard generator coefficients, the rule computes the gradient as
∂⟨H^⟩∂θk=12[⟨H^⟩(θk+s/2)−⟨H^⟩(θk−s/2)], \frac{\partial \langle \hat{H} \rangle}{\partial \theta_k} = \frac{1}{2} \left[ \langle \hat{H} \rangle (\theta_k + s/2) - \langle \hat{H} \rangle (\theta_k - s/2) \right],
where s=πs = \pi is the shift parameter, requiring two shifted evaluations per parameter.[21] This technique avoids numerical differentiation errors and scales linearly with the number of parameters, though it doubles the quantum oracle calls compared to energy evaluations alone; extensions to multi-parameter gates use stochastic sampling for efficiency.[22] The hybrid quantum-classical loop orchestrates this by repeatedly invoking the quantum device as an oracle to compute E(θ)E(\boldsymbol{\theta}) or shifted expectations, followed by classical updates to θ\boldsymbol{\theta} until convergence. This iterative feedback, often implemented in frameworks like Qiskit or PennyLane, ensures the ansatz evolves toward lower energies while mitigating quantum hardware limitations.[10] Convergence is typically monitored through criteria such as an energy threshold (e.g., achieving chemical accuracy of 1.6 mHartree relative to the exact ground state) or a small gradient norm (e.g., ∥∇E(θ)∥<10−6\|\nabla E(\boldsymbol{\theta})\| < 10^{-6}), halting the optimization when either is satisfied to balance accuracy and resource use.[10] Local minima, a common challenge due to the non-convex energy landscape, are addressed via warm-starting techniques that initialize θ\boldsymbol{\theta} near promising regions using approximations from classical solvers, prior VQE runs on similar systems, or reduced ansatzes, thereby accelerating convergence and improving solution quality.[23] For example, warm-starting with solutions from simpler molecular geometries has been shown to reduce iteration counts by providing better initial guesses, enhancing overall efficiency in practical VQE applications.

Mathematical Formulation

Core Equations

The variational quantum eigensolver (VQE) relies on minimizing a parameterized cost function to approximate the ground-state energy E0E_0 of a quantum Hamiltonian HH. The core cost function is the expectation value of the Hamiltonian with respect to a parameterized trial wavefunction ∣ψ(θ)⟩|\psi(\theta)\rangle, given by
E(θ)=⟨ψ(θ)∣H∣ψ(θ)⟩, E(\theta) = \langle \psi(\theta) | H | \psi(\theta) \rangle,
assuming the trial state is normalized such that ⟨ψ(θ)∣ψ(θ)⟩=1\langle \psi(\theta) | \psi(\theta) \rangle = 1.[24][25] In the more general unnormalized form introduced in the original formulation, it is the Rayleigh quotient
E(θ)=⟨ψ(θ)∣H∣ψ(θ)⟩⟨ψ(θ)∣ψ(θ)⟩, E(\theta) = \frac{\langle \psi(\theta) | H | \psi(\theta) \rangle}{\langle \psi(\theta) | \psi(\theta) \rangle},
which is minimized over the variational parameters θ\theta to yield an upper bound on E0E_0.[24] The expectation value ⟨H⟩\langle H \rangle can be expressed using the density operator ρ(θ)=∣ψ(θ)⟩⟨ψ(θ)∣\rho(\theta) = |\psi(\theta)\rangle \langle \psi(\theta) |, as
⟨H⟩=Tr[ρ(θ)H]. \langle H \rangle = \mathrm{Tr} [\rho(\theta) H].
For practical computation on quantum hardware, the Hamiltonian is typically decomposed into a linear combination of Pauli operators: H=∑kckPkH = \sum_k c_k P_k, where ckc_k are real coefficients and PkP_k are tensor products of Pauli matrices. The expectation then becomes
⟨H⟩=∑kck⟨Pk⟩, \langle H \rangle = \sum_k c_k \langle P_k \rangle,
with each ⟨Pk⟩=⟨ψ(θ)∣Pk∣ψ(θ)⟩\langle P_k \rangle = \langle \psi(\theta) | P_k | \psi(\theta) \rangle obtained via quantum measurements.[24][25][2] At the variational minimum, the condition ∂E∂θj=0\frac{\partial E}{\partial \theta_j} = 0 holds for each parameter θj\theta_j. Differentiating the cost function yields
∂E∂θj=2Re[⟨∂ψ(θ)∂θj∣H−E(θ)∣ψ(θ)⟩]=0, \frac{\partial E}{\partial \theta_j} = 2 \mathrm{Re} \left[ \left\langle \frac{\partial \psi(\theta)}{\partial \theta_j} \Big| H - E(\theta) \Big| \psi(\theta) \right\rangle \right] = 0,
which leverages the Hellmann-Feynman theorem in the context of parameter-shift rules or finite-difference approximations for gradient evaluation during classical optimization.[25][2] The variational principle guarantees convergence to the exact ground-state energy: E(θ)≥E0E(\theta) \geq E_0 for any trial state, with equality achieved in the limit as the ansatz ∣ψ(θ)⟩|\psi(\theta)\rangle spans the full Hilbert space of the system.[24][25]

Algorithm Steps

The variational quantum eigensolver (VQE) operates through an iterative hybrid quantum-classical procedure to approximate the ground state energy of a given Hamiltonian. The steps are outlined below in a structured format, drawing from the original formulation and subsequent refinements for practical implementation.[10]
  1. Encode the Hamiltonian into Pauli observables. The input Hamiltonian HH, representing the quantum system of interest (e.g., from quantum chemistry or materials simulation), is transformed into a qubit-based representation. This involves mapping fermionic or other operators to a sum of Pauli strings: H=∑kckPkH = \sum_k c_k P_k, where ckc_k are coefficients and PkP_k are tensor products of Pauli matrices (I,X,Y,ZI, X, Y, Z). Common mappings include the Jordan-Wigner or Bravyi-Kitaev transformations to ensure the encoding is compatible with qubit hardware. This step enables the expectation value ⟨H⟩\langle H \rangle to be computed additively from measurements of the individual PkP_k.[10]
  2. Initialize ansatz parameters θ\theta. A parameterized quantum circuit, or ansatz, is selected to generate trial states within a variational manifold. The parameters θ\theta (e.g., rotation angles in the circuit) are initialized, often starting from a mean-field solution such as the Hartree-Fock approximation to provide a physically motivated initial guess close to the ground state. Random initialization may also be used in some cases.[10]
  3. Quantum evaluation: Prepare ∣ψ(θ)⟩|\psi(\theta)\rangle and measure ⟨H⟩\langle H \rangle via Pauli grouping. The trial state ∣ψ(θ)⟩|\psi(\theta)\rangle is prepared on a quantum processor by executing the ansatz circuit with the current θ\theta. The energy E(θ)=⟨ψ(θ)∣H∣ψ(θ)⟩E(\theta) = \langle \psi(\theta) | H | \psi(\theta) \rangle is estimated by measuring the expectation values ⟨Pk⟩\langle P_k \rangle for each Pauli term, typically grouped into commuting sets to minimize the number of distinct quantum circuits required (e.g., via qubit-wise or full commutativity partitioning). Multiple shots are performed per group to reduce statistical error. Measurement techniques are detailed in the Measurement Protocol section.[10]
  4. Classical update: Optimize θ\theta to minimize E(θ)E(\theta); iterate until convergence. The estimated E(θ)E(\theta) is passed to a classical computer, where an optimization routine (e.g., gradient-based or derivative-free methods) updates θ\theta to lower the energy. This quantum-classical feedback loop repeats, with the quantum evaluation providing the objective function evaluations for the optimizer.[10]
Upon termination—typically when the change in E(θ)E(\theta) falls below a predefined threshold or a maximum iteration count is reached—the algorithm outputs the minimized energy as an approximation to the ground state eigenvalue and the final ∣ψ(θ)⟩|\psi(\theta)\rangle as the corresponding eigenvector, which can be reconstructed classically or via additional quantum measurements.[10]

Implementations and Examples

Basic Example

A basic example of the variational quantum eigensolver (VQE) involves computing the ground state energy of the hydrogen molecule (H₂) at its equilibrium bond length of 0.74 Å. This system is modeled with the STO-3G minimal basis set, yielding 4 spin-orbitals to describe the 2 electrons. The second-quantized electronic Hamiltonian is mapped to a qubit operator via the Jordan-Wigner transformation, resulting in a minimal model comprising 4 Pauli terms.[26][27] The variational ansatz employs the unitary coupled cluster singles and doubles (UCCSD) form, featuring 2 parameters corresponding to the single and double excitation amplitudes. This ansatz is prepared starting from the Hartree-Fock reference state, which serves as the initial trial wavefunction. A classical optimizer, such as sequential least squares programming, iteratively adjusts the parameters to minimize the expectation value of the encoded Hamiltonian.[26][27] Upon convergence, the VQE yields a ground state energy of approximately -1.136 Hartree, aligning closely with the full configuration interaction (FCI) benchmark for this system and demonstrating chemical accuracy within 1.6 mHartree. This example highlights VQE's ability to approximate molecular ground states using a compact ansatz and limited qubit resources.[28][29]

Software Tools

Several open-source software libraries and frameworks have been developed to facilitate the implementation of the variational quantum eigensolver (VQE), enabling both simulations on classical hardware and execution on quantum devices. These tools typically provide modules for constructing quantum Hamiltonians, designing parameterized ansatze, performing measurements, and integrating classical optimization routines, often with support for noisy intermediate-scale quantum (NISQ) devices. Qiskit, developed by IBM, includes comprehensive support for VQE through its Qiskit Nature module (formerly part of Qiskit Aqua), which offers pre-built ansatze such as the unitary coupled-cluster (UCC) singles and doubles (UCCSD) for quantum chemistry applications. This framework allows users to simulate VQE workflows on classical backends or execute them on IBM's cloud-accessible quantum hardware, with built-in error mitigation techniques like zero-noise extrapolation. Qiskit's integration with the Optimization module further enables the use of classical solvers such as COBYLA or SPSA for minimizing the variational energy. Cirq, Google's quantum computing framework, provides VQE implementations through its experiments and contrib packages, including tutorials for variational algorithms, supporting the creation of custom circuits and expectation value computations for molecular Hamiltonians.[30] It integrates seamlessly with TensorFlow Quantum for automatic differentiation, allowing efficient computation of parameter gradients essential for gradient-based optimization in VQE. Cirq's design emphasizes flexibility for NISQ devices, including support for Google's quantum processors through the Cirq Google extension. PennyLane, from Xanadu, specializes in differentiable quantum programming and supports hybrid quantum-classical VQE implementations through its quantum machine learning primitives. Users can define VQE circuits with hardware-agnostic ansatze, including hardware-efficient ones such as a two-qubit ansatz with two layers where each layer applies RY rotations to each wire followed by a CZ entangler on wires [0,1], using templates like StronglyEntanglingLayers or custom implementations,[31] and leverage built-in optimizers like Adam or Rotosolve, with automatic differentiation for both quantum and classical components. Notably, PennyLane extends VQE to photonic quantum hardware via plugins for platforms like Strawberry Fields, enabling simulations of continuous-variable systems alongside discrete qubit-based approaches. OpenFermion, an open-source library focused on quantum simulations of fermionic systems, provides essential tools for VQE by facilitating the construction of second-quantized Hamiltonians from molecular integrals and generating fermionic ansatze like Jordan-Wigner or Bravyi-Kitaev transformations. It serves as a foundational layer that integrates with higher-level frameworks such as Qiskit or Cirq for full VQE pipelines, emphasizing accurate mapping of quantum chemistry problems to qubit operators without direct hardware execution.

Applications

Quantum Chemistry

The variational quantum eigensolver (VQE) plays a pivotal role in quantum chemistry by enabling the simulation of molecular electronic structures through the approximation of ground-state wavefunctions and energies. By optimizing a parameterized quantum circuit to minimize the expectation value of the molecular Hamiltonian—encoded into qubit operators via fermion-to-qubit mappings such as the Jordan-Wigner transformation—VQE facilitates accurate predictions for systems intractable on classical computers. Early applications focused on small molecules, demonstrating VQE's potential to rival high-level ab initio methods. For ground-state energies, VQE using the unitary coupled-cluster singles and doubles (UCCSD) ansatz in the STO-3G basis has computed values for LiH and BeH2 within chemical accuracy (1.6 mHa) of full configuration interaction (FCI) benchmarks, requiring 12 qubits for LiH and 14 for BeH2. These results highlight VQE's efficacy for molecules with up to 6 electrons, where the UCCSD ansatz captures strong correlation effects effectively. Dissociation curves for LiH, generated by varying bond lengths from 0.7 to 10 Å, show VQE/UCCSD potential energy profiles with errors below 0.07 Ha relative to FCI, closely reproducing the avoided crossing and bond dissociation behavior observed in classical computations. Similar accuracy is achieved for BeH2 linear configurations, underscoring VQE's utility in mapping potential energy surfaces for reactive processes. Extensions of VQE, such as adaptive VQE-X, target excited states by iteratively building ansätze that minimize energy variance while ensuring orthogonality to lower eigenstates, extending the ground-state framework to higher-energy spectra. For LiH in the STO-3G basis, VQE-X variants compute the first singlet and triplet excited states with chemical accuracy against FCI using 12 qubits, enabling the study of photochemical transitions. Reaction energies, crucial for understanding chemical reactivity, have been estimated via VQE; for example, the hydrogenation of cyclohexadiene (C6H8 + H2) yields barriers and exothermicity in semi-quantitative agreement with FCI, differing by less than 0.01 Ha at key geometries. To address larger systems, VQE integrates with classical embedding techniques, such as density matrix embedding theory (DMET), which partitions molecules into a quantum-active subspace and a classically treated environment, reducing qubit demands. Embedded VQE applied to C6H8 + H2 achieves coupled-cluster singles and doubles (CCSD) accuracy using 16 qubits, compared to 68 for the full system, while correctly predicting equilibrium geometries for polyynes like C18 with 16 qubits versus 144. A 2024 advance, fragment molecular orbital-based VQE (FMO-VQE), further enhances scalability by fragmenting molecules and applying VQE to monomer and dimer interactions; for neutral hydrogen clusters like H6 and anionic H5- in the 6-31G basis, it delivers ground-state energies and reaction energies with errors below 0.2 mHa relative to CCSD, using at most 8 qubits. Benchmarks indicate VQE matches CCSD(T) accuracy for small molecules (e.g., LiH errors <1 mHa), but scales less favorably for larger systems without hybridization. For molecules with more than 10 orbitals, such as C2H4 (14 spatial orbitals in STO-3G), 28–32 qubits are typically required for chemical accuracy versus FCI, though embedding reduces this to 10–16 qubits while maintaining CCSD-level precision. These integrations position VQE as a hybrid tool for realistic quantum chemistry simulations beyond current classical limits.

Other Domains

The variational quantum eigensolver (VQE) has found significant applications in condensed matter physics, particularly for modeling spin systems described by Heisenberg and Ising Hamiltonians. In studies of antiferromagnetic Heisenberg models on frustrated lattices like the Kagome structure, VQE has been employed to approximate ground states, revealing insights into highly degenerate energy landscapes and magnetic ordering. For instance, simulations on IBM quantum hardware have demonstrated VQE's ability to prepare states for small Kagome lattices, achieving energies close to classical benchmarks despite noise. Similarly, the transverse-field Ising model has been simulated using VQE to probe quantum phase transitions, such as those induced by boundary conditions or frustration in two-dimensional systems. Recent 2025 experiments on superconducting qubits have shown VQE capturing phase boundaries with chemical accuracy for chains up to 10 spins, highlighting its utility in studying glassy dynamics and ordered phases. In optimization problems, VQE has been integrated with the quantum approximate optimization algorithm (QAOA) in hybrid frameworks to tackle combinatorial challenges. These hybrids encode problems like graph coloring or traveling salesperson instances into Ising-like Hamiltonians, where VQE optimizes variational parameters to minimize objective functions. A 2025 study applied such a QAOA-VQE approach to benchmark instances, outperforming classical heuristics in approximation ratios for moderately sized graphs on noisy intermediate-scale quantum devices. Additionally, VQE facilitates solving partial differential equations (PDEs) by discretizing them into Hamiltonians, as demonstrated in 2025 work on the advection-diffusion equation. Here, VQE on a finite-difference grid approximates ground-state solutions, yielding velocity fields with errors below 5% for low Reynolds numbers, offering a quantum advantage in high-dimensional fluid dynamics simulations. Within materials science, VQE has advanced the computation of electronic properties in periodic systems, including band structures and defect energies. For quasiparticle band structures in strongly correlated materials, a 2025 hybrid quantum-classical method combined VQE with quantum subspace expansion to calculate dispersion relations in Hubbard models, achieving convergence to within 1 meV of density functional theory results for small supercells. Tailored ansatze for multi-band tight-binding Hamiltonians have enabled VQE to determine band gaps in metal-halide perovskites, with 2025 simulations on 20-qubit circuits providing accurate predictions for finite-sized systems relevant to photovoltaics. Regarding defects, VQE has been used to evaluate spin defect energies in solid-state hosts, such as nitrogen-vacancy centers in diamond; a 2025 ADAPT-VQE implementation reduced qubit overhead while estimating formation energies to chemical precision, aiding quantum sensing applications. In high-energy physics, VQE approximations have been explored for lattice quantum chromodynamics (QCD), focusing on Yang-Mills vacua and gauge theories. Early applications targeted SU(3) plaquette chains, where VQE prepared low-energy states with fidelity exceeding 90% on small lattices. More recent 2025 efforts incorporated irreducible representations and perturbation theory to mitigate barren plateaus, enabling VQE to approximate ground states of lattice QCD Hamiltonians for volumes up to 2x2x2, with applications to hadron spectroscopy and confinement properties.

Advantages and Challenges

Benefits

The variational quantum eigensolver (VQE) is particularly well-suited for noisy intermediate-scale quantum (NISQ) devices due to its reliance on shallow quantum circuits that require shorter coherence times and exhibit greater tolerance to noise compared to algorithms like quantum phase estimation, thereby eliminating the immediate need for full quantum error correction.[1] This design allows VQE to leverage current quantum hardware with limited qubit counts, typically in the range of tens to hundreds of qubits, while maintaining computational viability in the presence of imperfect gates and decoherence.[6] A key strength of VQE lies in its hybrid quantum-classical architecture, which offloads the computationally intensive optimization process—such as parameter updates via classical routines like gradient descent—to a conventional computer, while the quantum processor handles state preparation and energy evaluation.[1] This division not only reduces the burden on the quantum hardware but also enables iterative refinement of trial wavefunctions through repeated short quantum measurements, trading extended quantum coherence for a polynomial increase in classical processing overhead.[6] VQE offers substantial flexibility through its use of parameterized ansatz circuits, which can be tailored to specific problem domains using chemically inspired forms like unitary coupled cluster or hardware-efficient constructions, allowing adaptation to both the molecular system and the underlying quantum architecture.[6] Beyond ground-state energies, the approach facilitates additional insights such as state tomography by estimating reduced density matrices or excited states via extensions like quantum subspace methods, providing a versatile framework for quantum chemistry analysis.[6] Regarding scalability, VQE holds promise for achieving chemical accuracy—defined as energies within 1.6 mHa of the exact value—for systems requiring more than 50 qubits when combined with error mitigation techniques such as zero-noise extrapolation or probabilistic error cancellation, which correct for noise with modest additional measurement costs. As of 2025, demonstrations have extended to systems requiring up to 50 qubits using advanced variants like FAST-VQE.[6][32] This potential stems from the variational principle's guarantee of an upper bound on the energy, enabling progressive improvements as hardware advances.[1]

Limitations

The variational quantum eigensolver (VQE) is highly sensitive to noise inherent in current noisy intermediate-scale quantum (NISQ) devices, where decoherence and gate errors degrade the accuracy of expectation value measurements. To achieve reliable estimates amid statistical fluctuations and systematic biases from imperfect operations, VQE typically requires more than 10^3 measurement shots per Pauli term in the Hamiltonian, with final energy evaluations often demanding up to 10^5 shots to approach chemical accuracy of 1.6 mHa. This overhead arises because decoherence shortens circuit depths to mere 0–2 layers for optimal performance, limiting the expressivity of trial states and amplifying errors in larger systems. A major trainability challenge in VQE stems from barren plateaus, where the variance of gradients in the cost function vanishes exponentially with the number of qubits N, scaling as O(2^{-N}) for random parameter initializations in expressive ansätze. This phenomenon, prevalent in deep parameterized quantum circuits, makes classical optimization inefficient as parameter updates become vanishingly small, often below chemical precision thresholds. While mitigations like careful initialization strategies can alleviate this issue for shallow circuits, it remains a fundamental barrier for scaling to problem sizes beyond tens of qubits.[33] Resource demands in VQE scale unfavorably for quantum chemistry applications, with the second-quantized molecular Hamiltonian decomposing into O(N^4) Pauli terms under the Jordan-Wigner mapping, where N is the number of spin orbitals. This results in a measurement overhead linear in the number of terms, requiring O(N^4 / \epsilon^2) total shots per optimization iteration for precision \epsilon, though grouping techniques can reduce it to O(N^3). Circuit depths and gate counts further escalate with ansatz complexity, such as O(N^4) two-qubit gates for unitary coupled-cluster ansätze, constraining practical implementations to small molecules like H_2 or LiH on current hardware.[34] VQE's optimization landscape is non-convex and prone to local minima, heavily dependent on the choice of ansatz, which may not span the full Hilbert space and thus yields only an upper bound to the true ground-state energy per the variational principle. Incomplete or poorly expressive ansätze, such as hardware-efficient variants, can trap the algorithm in suboptimal solutions, with convergence rates deteriorating exponentially in system size due to narrow gorges in the energy landscape. This ansatz reliance underscores the need for problem-tailored trial states, yet no universal choice guarantees global optimality.[1][34]

Recent Developments

Variants and Improvements

Since its inception, the variational quantum eigensolver (VQE) has seen significant enhancements through adaptive ansatz constructions that dynamically build parameterized quantum circuits tailored to the problem at hand. The adaptive derivative-assembled pseudo-Trotter (ADAPT)-VQE algorithm, introduced in 2019, iteratively selects operators based on their gradient magnitudes to construct a compact ansatz, reducing circuit depth while improving convergence for molecular ground states.[17] Extensions in 2023 have refined ADAPT-VQE by incorporating problem-tailored operator pools that account for local electronic structure, enhancing accuracy on noisy intermediate-scale quantum (NISQ) hardware for systems like the hydrogen molecule.[35] Another 2023 variant, Overlap-ADAPT-VQE, leverages overlap measurements between trial states and reference configurations to further optimize ansatz selection, demonstrating reduced parameter counts and better scalability for quantum chemistry simulations.[36] To address optimization challenges such as barren plateaus—regions in the parameter landscape where gradients vanish, hindering training—a cyclic VQE framework was proposed in 2025. This approach employs a hardware-efficient ansatz with measurement-driven feedback loops that enable "staircase descent," allowing escape from plateaus by cyclically adjusting parameters in a structured manner, achieving ground-state energies within 1% chemical accuracy for up to 20-qubit systems on simulated NISQ devices.[37] Error mitigation techniques have been integrated into VQE workflows to counteract noise in NISQ environments without requiring full error correction. QDRIFT, a randomized simulation method that averages noisy circuit outcomes to approximate ideal expectations, has been applied to VQE for ground-state estimation in noisy simulations.[38] Complementing this, probabilistic error cancellation (PEC) inverts the noise channel by sampling quasi-probability distributions of gate sequences, yielding unbiased estimates; when combined with VQE, PEC has mitigated errors in various platforms, with feasible sampling overheads.[39] A 2025 distributed VQE variant extends these mitigations across networked quantum processors for quadratic unconstrained binary optimization (QUBO) problems, partitioning circuits for parallel computation while synchronizing via classical links.[40] Specialized VQE adaptations target non-standard quantum states and optimization paradigms. The variational quantum state eigensolver (VQSE), developed in 2022, extends VQE to mixed states by variationally optimizing the largest eigenvalues of density matrices, preparing corresponding eigenstates via a parameterized circuit; this has proven effective for thermal state simulations in open quantum systems, such as spin chains at finite temperatures.[41] Building on adaptive principles, a greedy gradient-free adaptive VQE (GGA-VQE) was introduced in 2025, which selects operators via analytic, derivative-free metrics like energy gradients, avoiding costly quantum gradient computations; tested on a 25-qubit trapped-ion device for Ising models, it converged to ground states 2-3 times faster than standard parameter-shift methods while maintaining circuit depths under 100 gates.[42] Efficiency improvements for large-scale applications have focused on fragmentation strategies. The fragment molecular orbital (FMO)-based VQE, proposed in 2024, divides macromolecules into interacting fragments, solving reduced Hamiltonians per fragment with VQE before perturbatively reconstructing the full energy; demonstrated on small systems like the water dimer, this approach efficiently utilizes qubits for quantum chemistry simulations.[43] As of November 2025, recent advances include full simulations of 50-qubit universal quantum computers for VQE benchmarking, parallelized Givens ansatzes for molecular ground states, and stabilizer-accelerated methods for many-body ground-state estimation, enhancing scalability on NISQ hardware.[44][45][46]

Experimental Realizations

One of the earliest experimental realizations of the variational quantum eigensolver (VQE) was demonstrated using trapped-ion qubits in 2018, where researchers implemented a scalable version to compute the ground-state energies of the H₂ and LiH molecules. This experiment, conducted on a digital quantum simulator with four qubits, achieved energies within 0.4% of the full configuration interaction values, marking the first hardware demonstration of VQE for quantum chemistry problems despite noise limitations.[47] In parallel, superconducting qubit platforms advanced VQE implementations starting in 2017, with experiments on IBM's early quantum processors optimizing up to six-qubit Hamiltonians derived from molecular structures like H₂ and LiH in minimal bases. These hardware-efficient ansatze, tailored to reduce circuit depth, yielded ground-state energies with errors below 1% relative to exact diagonalization, highlighting VQE's resilience to gate fidelities around 99%. A related demonstration involved preparing a five-qubit GHZ state as part of benchmarking VQE's performance on correlated systems.[14] Mid-scale experiments expanded to larger systems by 2019, exemplified by VQE runs on IBM's 20-qubit superconducting processor for simulating alkali metal hydrides such as LiH. Using a unitary coupled-cluster ansatz, these tests benchmarked VQE against classical methods, achieving chemical accuracy (error < 1.6 mHa) for small molecules after error mitigation, though circuit depths up to 100 gates posed challenges for NISQ hardware.[48] Photonic platforms contributed to VQE realizations around 2021, particularly for bosonic systems, with continuous-variable implementations on Xanadu's hardware encoding the ground state of the attractive Bose-Hubbard model for up to four modes. This approach leveraged Gaussian states and non-Gaussian operations to capture strong correlations, demonstrating fidelity improvements over mean-field approximations in noisy photonic setups.[49] Recent advancements by 2023 focused on scaling beyond 50 qubits with error mitigation techniques, as shown in trapped-ion experiments using purification methods to enhance VQE accuracy for pair-correlated electron models. On IonQ's 32-qubit Aria processor, these runs mitigated readout and gate errors, reducing energy estimation variance by factors of 10–100, enabling feasible approximations for systems requiring 50+ qubits without full fault tolerance.[50] Distributed VQE architectures have been explored in theoretical proposals as of 2024, including partitioning circuits across modules connected via limited entanglement links for scalability in multi-node setups.[51] Benchmarks in 2024 underscored VQE's progress toward chemical accuracy on real hardware, with photonic qudit-based experiments estimating LiH ground-state energies to within 0.036 Ha of full configuration interaction results using orbital-angular-momentum encoding on four qudits. Error rates around 5% were reported, with fidelities exceeding 90% for key gates, establishing VQE's viability for small-molecule quantum chemistry under NISQ constraints.[52]

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