REVIEW 23 cited by
Chemistry Beyond the Scale of Exact Diagonalization on a Quantum-Centric Supercomputer
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Signed reviews
abstract
A universal quantum computer can simulate diverse quantum systems, with electronic structure for chemistry offering challenging problems for practical use cases around the hundred-qubit mark. While current quantum processors have reached this size, deep circuits and large number of measurements lead to prohibitive runtimes for quantum computers in isolation. Here, we demonstrate the use of classical distributed computing to offload all but an intrinsically quantum component of a workflow for electronic structure simulations. Using a Heron superconducting processor and the supercomputer Fugaku, we simulate the ground-state dissociation of N$_2$ and the [2Fe-2S] and [4Fe-4S] clusters, with circuits up to 77 qubits and 10,570 gates. The proposed algorithm processes quantum samples to produce upper bounds for the ground-state energy and sparse approximations to the ground-state wavefunctions. Our results suggest that, for current error rates, a quantum-centric supercomputing architecture can tackle challenging chemistry problems beyond sizes amenable to exact diagonalization.
Forward citations
Cited by 23 Pith papers
-
Hardness of classically sampling quantum chemistry circuits
Single-layer unitary cluster Jastrow circuits can emulate quadratic-Ising IQP circuits, so sampling from some quantum chemistry ansaetze is worst-case classically hard unless the polynomial hierarchy collapses.
-
Towards quantum-centric simulations of extended molecules: sample-based quantum diagonalization enhanced with density matrix embedding theory
The first implementation of density matrix embedding theory with sample-based quantum diagonalization is demonstrated on quantum hardware for an 18-hydrogen ring and cyclohexane, yielding energies close to classical r...
-
Machine learning for sample-based quantum diagonalization: generative configuration recovery and the classical-simulability frontier
A critical review plus small exact-FCI experiments concludes that sample-based quantum diagonalization has not beaten classical selected CI and maps where, if anywhere, a quantum or generative advantage could survive.
-
Scaling a CUDA-Q GQE + QSCI pipeline to 40 qubits for EUV photoresist chemistry
A GQE+QSCI quantum-classical pipeline reaches chemical accuracy (<1.6 mHa) up to 32 qubits on SnO and 30 on a tin photoresist, with real-hardware results at 14 and 22 qubits.
-
Resource-efficient Quantum Algorithms for Selected Hamiltonian Subspace Diagonalization
A first-quantized CI-matrix QSCI variant reduces qubit count to O(log N) and gives accuracy on N2/naphthalene comparable to sample-based quantum diagonalization.
-
Analyzing Common Electronic Structure Theory Algorithms for Distributed Quantum Computing
Under local-operation circuit cutting, only the LUCJ ansatz among five tested electronic structure methods shows practical sampling overhead.
-
Quantum-Classical Auxiliary Field Quantum Monte Carlo with Matchgate Shadows on Trapped Ion Quantum Computers
The largest quantum-classical AFQMC run to date, with a 16-qubit trial state on IonQ Forte, estimates a nickel reaction barrier within ±4 kcal/mol of CCSD(T) on an ideal simulator, but is 10 kcal/mol off with a revers...
-
Scalable Simulation of Quantum Many-Body Dynamics with Or-Represented Quantum Algebra
ORQA, a Pauli-string based simulation framework, is parallelized to run on Fugaku with up to 2^17 processes, tracking over a trillion Pauli strings and reproducing kicked Ising results.
-
Programming optical-lattice Fermi-Hubbard quantum simulators
Pre-compiled variational and imaginary-time circuits built from native optical-lattice Fermi-Hubbard dynamics prepare ground states of local and extended Hubbard models on ladders with high fidelity in shorter times t...
-
Quantum computing in spin-adapted representations for efficient simulations of spin systems
Spin-path truncation plus symmetric-group rules yields sparse local qubit Hamiltonians for the Heisenberg model, with shallow adiabatic circuits reaching about 99 percent fidelity for N=16.
-
Polynomially efficient quantum enabled variational Monte Carlo for training neural-network quantum states for physico-chemical applications
A variational Monte Carlo algorithm that trains a restricted Boltzmann machine quantum state by sampling a fitted Ising surrogate with a Trotterized quantum circuit, demonstrated on small spin and molecular systems.
-
Multi-QIDA method for VQE state preparation in molecular systems
Multi-QIDA, a layered ansatz built from quantum mutual information of classical RCISD wavefunctions, outperforms the ladder hardware-efficient ansatz at matched CNOT count on five small molecular systems in noiseless ...
-
Enhanced Prediction of CAR T-Cell Cytotoxicity with Quantum-Kernel Methods
A 61-qubit projected quantum kernel classifier modestly outperforms a classical SVM at predicting high versus low CAR T-cell cytotoxicity, with the largest per-motif gains in data-poor positions.
-
Coupled cluster method tailored by quantum selected configuration interaction
QSCI-TCC combines quantum-selected CI with tailored coupled-cluster to achieve accurate bond-breaking energies while using far fewer measurement shots.
-
Towards secondary structure prediction of longer mRNA sequences using a quantum-centric optimization scheme
Hybrid CVaR and IQP quantum workflows find CPLEX-verified optimal solutions for mRNA-folding QUBO instances up to 156 qubits, but simulated scaling shows steeply declining success rates.
-
A Multi-Scale Quantum Framework for Evaluating Metal-Organic Frameworks in Carbon Capture
A hierarchical cluster model combined with embedded CCSD/MP2 calculations reproduces experimental CO2 heats of adsorption for five M2(dobdc) MOFs to a mean absolute error near 2 kcal/mol.
-
Symmetry-adapted sample-based quantum diagonalization: Application to lattice model
A symmetry-adapted postprocessing step for sample-based quantum diagonalization improves energy convergence on the two-leg Hubbard ladder when combined with a momentum basis.
-
Qubit-efficient quantum chemistry with the ADAPT variational quantum eigensolver and double unitary downfolding
DUCC effective Hamiltonians combined with ADAPT-VQE recover dynamical correlation energy outside the active space with similar ADAPT iteration counts as bare Hamiltonians, but the gains rely on classical CCSD amplitud...
-
Implicit solvent sample-based quantum diagonalization
SQD with IEF-PCM solvation reproduces CASCI IEF-PCM energies for four small molecules on IBM quantum hardware.
-
Addressing the Readout Problem in Quantum Differential Equation Algorithms with Quantum Scientific Machine Learning
Quantum neural networks can classify shock and turbulent flow solutions encoded as quantum states, with accuracy strongly dependent on Fourier versus real-space basis choice.
-
Benchmarking Quantum Simulations of the Lipkin-Meshkov-Glick Model Using Large Tensor Networks
DMRG yields LMG ground energies to N=1400; on IBM hardware SQD stays within ~1% to ~17 particles while VQE does so only near N=6.
-
GPU-Accelerated Distributed QAOA on Large-scale HPC Ecosystems
GPU-accelerated DQAOA with impact-factor based decomposition runs up to 10x faster than CPU simulations on Frontier, with better scaling up to 160 devices.
-
Interfacing Quantum Computing Systems with High-Performance Computing Systems: An Overview
A broad literature review of architectures, software stacks, and challenges for integrating quantum processors into high-performance computing systems.
Discussion (0). Continue with ORCID to comment.