Qubit-efficient simulation of thermal states with quantum tensor networks

Zhang, Yuxuan; Jahanbani, Shahin; Daoheng, Niu,; Haghshenas, Reza; Potter, Andrew C.

doi:10.1103/physrevb.106.165126

Cited by 6 publications

(2 citation statements)

References 32 publications

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“…The experimental requirements for implementing QRNNs are comparable with those demonstrated in producing matrix-product states and density matrices [92][93][94][95][96]. Indeed, in [92], the authors have experimentally implemented a quantum channel with memory for 6 time steps on an ion-trap quantum computer. QRNNs are also suitable for some existing measurement-based photonic devices.…”

Section: Discussionmentioning

confidence: 99%

Learning Quantum Processes with Memory -- Quantum Recurrent Neural Networks

Bondarenko¹,

Salzmann²,

Schmiesing³

2023

Preprint

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Recurrent neural networks play an important role in both research and industry. With the advent of quantum machine learning, the quantisation of recurrent neural networks has become recently relevant. We propose fully quantum recurrent neural networks, based on dissipative quantum neural networks, capable of learning general causal quantum automata. A quantum training algorithm is proposed and classical simulations for the case of product outputs with the fidelity as cost function are carried out. We thereby demonstrate the potential of these algorithms to learn complex quantum processes with memory in terms of the exemplary delay channel, the time evolution of quantum states governed by a time-dependent Hamiltonian, and highand low-frequency noise mitigation. Numerical simulations indicate that our quantum recurrent neural networks exhibit a striking ability to generalise from small training sets.

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Section: Discussionmentioning

confidence: 99%

Learning Quantum Processes with Memory -- Quantum Recurrent Neural Networks

Bondarenko¹,

Salzmann²,

Schmiesing³

2023

Preprint

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“…4. These circuits appear naturally when simulating local 1D Hamiltonians, and qubit reuse in such linear circuits has been explored previously in the context of quantum matrix product states [24,28,[38][39][40] and their time evolution [12].…”

Section: Local Brickwork Circuits In 1dmentioning

confidence: 99%

Qubit-reuse compilation with mid-circuit measurement and reset

DeCross¹,

Chertkov²,

Kohagen³

et al. 2022

Preprint

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A number of commercially available quantum computers, such as those based on trapped-ion or superconducting qubits, can now perform mid-circuit measurements and resets. In addition to being crucial for quantum error correction, this capability can help reduce the number of qubits needed to execute many types of quantum algorithms by measuring qubits as early as possible, resetting them, and reusing them elsewhere in the circuit. In this work, we introduce the idea of qubit-reuse compilation, which takes as input a quantum circuit and produces as output a compiled circuit that requires fewer qubits to execute due to qubit reuse. We present two algorithms for performing qubit-reuse compilation: an exact constraint programming optimization model and a greedy heuristic. We introduce the concept of dual circuits, obtained by exchanging state preparations with measurements and vice versa and reversing time, and show that optimal qubit-reuse compilation requires the same number of qubits to execute a circuit as its dual. We illustrate the performance of these algorithms on a variety of relevant near-term quantum circuits, such as one-dimensional and two-dimensional time-evolution circuits, and numerically benchmark their performance on the quantum adiabatic optimization algorithm (QAOA) applied to the MaxCut problem on random three-regular graphs. To demonstrate the practical benefit of these techniques, we experimentally realize an 80-qubit QAOA MaxCut circuit on the 20-qubit Quantinuum H1-1 trapped ion quantum processor using qubit-reuse compilation algorithms.

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