Phasecraft›Phasecraft› offers Mondrian

Mondrian uses quantum-generated starting points to accelerate established classical optimization methods. This approach lets quantum calculations contribute to problems that cannot fit directly onto small quantum devices.

Its energy applications were developed with the UK's National Energy System Operator. The public tool lets users compare results through a Q-factor that relates expected classical and quantum-enhanced algorithm running times.

Mondrian's published Q-factor comparison measures the optimization algorithm and excludes the time needed to execute the quantum algorithm itself. The displayed speedup therefore does not establish an equivalent reduction in total end-to-end computing time.

Live experimentation on custom problem instances uses classical emulation and requires authorization. The demonstration compares against the Palubeckis MST1 algorithm in MQLib, making the stated baseline part of interpreting its results.

Mondrian presents prepared network problems alongside an authorized interface for experimenting with custom instances. Its energy examples include placing monitoring equipment and identifying critical nodes within a network.

Organizations seeking the full version contact Phasecraft to discuss access and potential applications. The platform uses a hybrid workflow in which quantum-derived initial solutions feed subsequent classical optimization.

Mondrian combines quantum algorithms with classical optimization to solve network problems. Its public interface includes prepared examples and provides authorized users with classical emulation for their own problem instances.

The platform applies the approach to energy networks, including network partitioning, critical node identification and renewable storage placement. Phasecraft developed these use cases with the UK's National Energy System Operator.