Densely connected tensor network geometries train to lower infidelity than sparse ones on random quantum states, and a new leaf-contraction trick reduces memory while improving training.
Memory-Efficient Quantum Circuit Simulation by Using Lossy Data Compression
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abstract
In order to evaluate, validate, and refine the design of new quantum algorithms or quantum computers, researchers and developers need methods to assess their correctness and fidelity. This requires the capabilities of quantum circuit simulations. However, the number of quantum state amplitudes increases exponentially with the number of qubits, leading to the exponential growth of the memory requirement for the simulations. In this work, we present our memory-efficient quantum circuit simulation by using lossy data compression. Our empirical data shows that we reduce the memory requirement to 16.5% and 2.24E-06 of the original requirement for QFT and Grover's search, respectively. This finding further suggests that we can simulate deep quantum circuits up to 63 qubits with 0.8 petabytes memory.
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Advantages of density in tensor network geometries for gradient based training
Densely connected tensor network geometries train to lower infidelity than sparse ones on random quantum states, and a new leaf-contraction trick reduces memory while improving training.