Processor and method for performing tensor network contraction in quantum simulator

A quantum simulator for simulating a quantum circuit, wherein the quantum simulator comprises a non-transitory memory storing instructions and a processor for a quantum simulator, wherein when the processor executes the instructions, the quantum simulator is configured to:

• perform a local search algorithm to determine a plurality of contraction expressions, wherein each contraction expression is suitable to contract a respective tensor network of one or more tensor networks into a determined contracted tensor network;
• determine, for each contraction expression, a contraction cost for contracting the respective tensor network into the determined contracted tensor network based on a cost function;
• select (the contraction expression with the lowest contraction cost;
• and
• contract each tensor network of the one or more tensor networks into the determined contracted tensor network based on the selected contraction expression;
• wherein the cost function is based on at least on of:
• a first parameter indicating an amount of memory required for contracting the respective tensor network into the determined contracted tensor network;
• a second parameter indicating a computational complexity required for contracting the respective tensor network into the determined contracted tensor network;
• and
• a third parameter indicating a number of read-write operations required for contracting the respective tensor network into the determined contracted tensor network.

A processing method for a quantum simulator, wherein the method comprises:

• performing a local search algorithm to determine a plurality of contraction expressions, wherein each contraction expression is suitable to contract a respective tensor network of one or more tensor networks into a determined contracted tensor network;
• determining, for each contraction expression, a contraction cost for contracting the respective tensor network into the determined contracted tensor network based on a cost function;
• selecting the contraction expression with the lowest contraction cost;
• and
• contracting each tensor network of the one or more tensor networks into the determined contracted tensor network based on the selected contraction expression;
• wherein the cost function is based on at least:
• a first parameter indicating an amount of memory required for contracting the respective tensor network into the determined contracted tensor network;
• a second parameter indicating a computational complexity required for contracting the respective tensor network into the determined contracted tensor network;
• and
• a third parameter indicating a number of read-write operations required for contracting the respective tensor network into the determined contracted tensor network.