Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

arXiv:2403.16810 · Journal of Chemical Theory and Computation

Holographic Gaussian Boson Sampling with Matrix Product States on 3D cQED Processors

Ningyi Lyu, Paul Bergold, Micheline B. Soley, Chen Wang, Víctor S. Batista

Published Jul 5, 2024• 6 citations
arXivHTMLPDF

Abstract

We introduce quantum circuits for simulations of multimode state vectors on 3D circuit quantum electrodynamics (cQED) processors using matrix product state representations. The circuits are demonstrated as applied to simulations of molecular docking based on holographic Gaussian boson sampling (GBS), as illustrated for the binding of a thiol-containing aryl sulfonamide ligand to the tumor necrosis factor-α converting enzyme receptor. We show that cQED devices with a modest number of modes could be employed to simulate multimode systems by repurposing working modes through measurement and reinitialization. We anticipate that a wide range of GBS applications could be implemented on compact 3D cQED processors analogously using the holographic approach. Simulations on qubit-based quantum computers could be implemented analogously using circuits that represent continuous variables in terms of truncated expansions of Fock states.

DOI
10.1021/acs.jctc.4c00384

Related papers

Simulation of Continuous-Variable Quantum Systems with Tensor Network

Ryutaro Nagai, Takao Tomono, Yuichiro Minato

Tensor network is known as a powerful method to simulate quantum computation on a classical computer. Recently, the random quantum circuit used in the demonstration of quantum supremacy by Sycamore quantum computer have been simulated with tensor network method. Compared to the application of tensor networks to Discrete-Variable (DV) quantum computation, there are very few examples of application of tensor network to Continuous-Variable (CV) quantum computation. Therefore, we applied a tensor network structure called Matrix Product States (MPS) to CV quantum computation. Tensor network is expected to be useful for simulating Gaussian Boson Sampling (GBS), which is a promised near-term application of photonic quantum system. We outline the performance of tensor network-based simulation of CV quantum computation in GBS.

IEEE International Conference on Quantum Computing and Engineering (QCE)•Oct 1, 2021•3 citations

Molecular docking with Gaussian Boson Sampling

Leonardo Banchi, Mark Fingerhuth, Tomas Babej +2 more

Gaussian Boson Samplers are photonic quantum devices with the potential to perform intractable tasks for classical systems. As with other near-term quantum technologies, an outstanding challenge is to identify specific problems of practical interest where these devices can prove useful. Here, we show that Gaussian Boson Samplers can be used to predict molecular docking configurations, a central problem for pharmaceutical drug design. We develop an approach where the problem is reduced to finding the maximum weighted clique in a graph, and show that Gaussian Boson Samplers can be programmed to sample large-weight cliques, i.e., stable docking configurations, with high probability, even with photon losses. We also describe how outputs from the device can be used to enhance the performance of classical algorithms. To benchmark our approach, we predict the binding mode of a ligand to the tumor necrosis factor-α converting enzyme, a target linked to immune system diseases and cancer.

arXiv:1902.00462•Science Advances•Jun 5, 2020•169 citations

Mapping Molecular Hamiltonians into Hamiltonians of Modular cQED Processors

Ningyi Lyu, Alessandro Miano, Ioannis Tsioutsios +7 more

We introduce a general method based on the operators of the Dyson-Masleev transformation to map the Hamiltonian of an arbitrary model system into the Hamiltonian of a circuit Quantum Electrodynamics (cQED) processor. Furthermore, we introduce a modular approach to programming a cQED processor with components corresponding to the mapping Hamiltonian. The method is illustrated as applied to quantum dynamics simulations of the Fenna-Matthews-Olson (FMO) complex and the spin-boson model of charge transfer. Beyond applications to molecular Hamiltonians, the mapping provides a general approach to implement any unitary operator in terms of a sequence of unitary transformations corresponding to powers of creation and annihilation operators of a single bosonic mode in a cQED processor.

arXiv:2306.06341•Journal of Chemical Theory and Computation•Sep 21, 2023•6 citations

Molecular Docking with Quantum Circuit Evolution

Gleydson Fernandes de Jesus, Bruno Oziel Fernandez, Marcelo A. Moret

Molecular docking is an important step in drug discovery, enabling the evaluation of receptor-ligand affinity while reducing experimental costs and increasing the number of possible tests. However, the high computational cost associated with molecular docking remains a limiting factor that can restrict both the experimental precision and the scale of the problems being addressed. To improve the future applicability of molecular docking, recent works have proposed the use of quantum algorithms based on Gaussian Boson Sampling quantum computers and also gate-based quantum computers. In this work, we propose the use of Quantum Circuit Evolution (QCE) for solving the molecular docking problem, a gate based and gradient-free quantum evolutionary method whose evolution is driven by the random application of unitary operations to a quantum circuit. The proposed algorithm demonstrated the ability to find the best solution to the problem in fewer steps than the methods presented in previous studies, exhibiting fast and stable convergence.

quant-pharXiv:2607.12060•Jul 15, 2026•0 citations

Efficient simulation of low-entanglement bosonic Gaussian states in polynomial time

Tong Liu, Hui-Ke Jin, Tao Xiang +1 more

Bosonic Gaussian states are ubiquitous in quantum optics and condensed matter physics. While they are efficiently handled within the Gaussian formalism, sampling requires calculating amplitudes in the boson occupation basis. This step, however, is hindered by a significant bottleneck due to the hafnian. We present an efficient algorithm that converts pure bosonic Gaussian states into matrix product states (MPSs), thereby establishing a versatile tool for probing bosonic Gaussian systems in settings where direct Gaussian-formalism-based calculations become inefficient. Our method combines a Gaussian singular value decomposition with a projected-creation-operator mapping that constructs local MPS tensors without computing hafnians. Benchmarking on covariance matrices from the Jiuzhang 2.0 and Jiuzhang 4.0 Gaussian boson sampling experiments demonstrates substantial speedups over previous tensor-network approaches in the low-entanglement regime relevant to lossy devices. The method provides a scalable classical simulation framework for bosonic Gaussian states with limited entanglement. In this regime, a target accuracy can be achieved with a bond dimension that remains computationally tractable, thereby extending the applicability of MPS-based methods to a broad range of bosonic systems.

quant-pharXiv:2512.10643•npj Quantum Information•Dec 12, 2025•0 citations

Sequential quantum simulation of spin chains with a single circuit QED device

Yuxuan Zhang, Shahin Jahanbani, Ameya Riswadkar +2 more

Quantum simulations of many-body systems in materials science and chemistry are promising application areas for quantum computers. However, the limited scale and coherence of near-term quantum processors pose a significant obstacle to realizing this potential. Here, we theoretically outline how a single-mode circuit quantum electrodynamics device, consisting of a transmon qubit coupled to a long-lived cavity mode, can be used to simulate the ground state of a highly entangled quantum many-body spin chain. We exploit recently developed methods for implementing quantum operations to sequentially build up a matrix product state (MPS) representation of a many-body state. This approach reuses the transmon qubit to read out the state of each spin in the chain and exploits the large state space of the cavity as a quantum memory encoding intersite correlations and entanglement. We show, through simulation, that analog (pulse-level) control schemes can accurately prepare a known MPS representation of a quantum critical spin chain in significantly less time than digital (gate-based) methods, thereby reducing the exposure to decoherence. We then explore this analog-control approach for the variational preparation of an unknown ground state. We demonstrate that the large state space of the cavity can be used to replace multiple qubits in a qubit-only architecture, and could therefore simplify the design of quantum processors for materials simulation. We explore the practical limitations of realistic noise and decoherence and discuss avenues for scaling this approach to more complex problems that challenge classical computational methods.

arXiv:2308.16229•Physical Review A•Feb 14, 2024•4 citations
We introduce quantum circuits for simulations of multimode state vectors on 3D circuit quantum electrodynamics (cQED) processors using matrix product state representations. The circuits are demonstrated as applied to simulations of molecular docking based on holographic Gaussian boson sampling (GBS), as illustrated for the binding of a thiol-containing aryl sulfonamide ligand to the tumor necrosis factor-α converting enzyme receptor. We show that cQED devices with a modest number of modes could be employed to simulate multimode systems by repurposing working modes through measurement and reinitialization. We anticipate that a wide range of GBS applications could be implemented on compact 3D cQED processors analogously using the holographic approach. Simulations on qubit-based quantum computers could be implemented analogously using circuits that represent continuous variables in terms of truncated expansions of Fock states.
Tensor network is known as a powerful method to simulate quantum computation on a classical computer. Recently, the random quantum circuit used in the demonstration of quantum supremacy by Sycamore quantum computer have been simulated with tensor network method. Compared to the application of tensor networks to Discrete-Variable (DV) quantum computation, there are very few examples of application of tensor network to Continuous-Variable (CV) quantum computation. Therefore, we applied a tensor network structure called Matrix Product States (MPS) to CV quantum computation. Tensor network is expected to be useful for simulating Gaussian Boson Sampling (GBS), which is a promised near-term application of photonic quantum system. We outline the performance of tensor network-based simulation of CV quantum computation in GBS.
Gaussian Boson Samplers are photonic quantum devices with the potential to perform intractable tasks for classical systems. As with other near-term quantum technologies, an outstanding challenge is to identify specific problems of practical interest where these devices can prove useful. Here, we show that Gaussian Boson Samplers can be used to predict molecular docking configurations, a central problem for pharmaceutical drug design. We develop an approach where the problem is reduced to finding the maximum weighted clique in a graph, and show that Gaussian Boson Samplers can be programmed to sample large-weight cliques, i.e., stable docking configurations, with high probability, even with photon losses. We also describe how outputs from the device can be used to enhance the performance of classical algorithms. To benchmark our approach, we predict the binding mode of a ligand to the tumor necrosis factor-α converting enzyme, a target linked to immune system diseases and cancer.
We introduce a general method based on the operators of the Dyson-Masleev transformation to map the Hamiltonian of an arbitrary model system into the Hamiltonian of a circuit Quantum Electrodynamics (cQED) processor. Furthermore, we introduce a modular approach to programming a cQED processor with components corresponding to the mapping Hamiltonian. The method is illustrated as applied to quantum dynamics simulations of the Fenna-Matthews-Olson (FMO) complex and the spin-boson model of charge transfer. Beyond applications to molecular Hamiltonians, the mapping provides a general approach to implement any unitary operator in terms of a sequence of unitary transformations corresponding to powers of creation and annihilation operators of a single bosonic mode in a cQED processor.
Molecular docking is an important step in drug discovery, enabling the evaluation of receptor-ligand affinity while reducing experimental costs and increasing the number of possible tests. However, the high computational cost associated with molecular docking remains a limiting factor that can restrict both the experimental precision and the scale of the problems being addressed. To improve the future applicability of molecular docking, recent works have proposed the use of quantum algorithms based on Gaussian Boson Sampling quantum computers and also gate-based quantum computers. In this work, we propose the use of Quantum Circuit Evolution (QCE) for solving the molecular docking problem, a gate based and gradient-free quantum evolutionary method whose evolution is driven by the random application of unitary operations to a quantum circuit. The proposed algorithm demonstrated the ability to find the best solution to the problem in fewer steps than the methods presented in previous studies, exhibiting fast and stable convergence.
Bosonic Gaussian states are ubiquitous in quantum optics and condensed matter physics. While they are efficiently handled within the Gaussian formalism, sampling requires calculating amplitudes in the boson occupation basis. This step, however, is hindered by a significant bottleneck due to the hafnian. We present an efficient algorithm that converts pure bosonic Gaussian states into matrix product states (MPSs), thereby establishing a versatile tool for probing bosonic Gaussian systems in settings where direct Gaussian-formalism-based calculations become inefficient. Our method combines a Gaussian singular value decomposition with a projected-creation-operator mapping that constructs local MPS tensors without computing hafnians. Benchmarking on covariance matrices from the Jiuzhang 2.0 and Jiuzhang 4.0 Gaussian boson sampling experiments demonstrates substantial speedups over previous tensor-network approaches in the low-entanglement regime relevant to lossy devices. The method provides a scalable classical simulation framework for bosonic Gaussian states with limited entanglement. In this regime, a target accuracy can be achieved with a bond dimension that remains computationally tractable, thereby extending the applicability of MPS-based methods to a broad range of bosonic systems.
Quantum simulations of many-body systems in materials science and chemistry are promising application areas for quantum computers. However, the limited scale and coherence of near-term quantum processors pose a significant obstacle to realizing this potential. Here, we theoretically outline how a single-mode circuit quantum electrodynamics device, consisting of a transmon qubit coupled to a long-lived cavity mode, can be used to simulate the ground state of a highly entangled quantum many-body spin chain. We exploit recently developed methods for implementing quantum operations to sequentially build up a matrix product state (MPS) representation of a many-body state. This approach reuses the transmon qubit to read out the state of each spin in the chain and exploits the large state space of the cavity as a quantum memory encoding intersite correlations and entanglement. We show, through simulation, that analog (pulse-level) control schemes can accurately prepare a known MPS representation of a quantum critical spin chain in significantly less time than digital (gate-based) methods, thereby reducing the exposure to decoherence. We then explore this analog-control approach for the variational preparation of an unknown ground state. We demonstrate that the large state space of the cavity can be used to replace multiple qubits in a qubit-only architecture, and could therefore simplify the design of quantum processors for materials simulation. We explore the practical limitations of realistic noise and decoherence and discuss avenues for scaling this approach to more complex problems that challenge classical computational methods.