Dirac-3 spec sheet

3rd Generation QUDIT Entropy Quantum Computer


Discrete optimization problems involve identifying the most favorable solution from a finite and discrete set of possibilities. Some prominent examples include the traveling salesman problem, knapsack problem, graph coloring, spanning tree, matching, set covering, and set packing.

These problems are applicable to a wide array of fields, including scheduling, logistics, network design, drug discovery, and data analysis. Many discrete optimization problems are NP-hard, that is, no known algorithm can efficiently solve all instances of the problem in polynomial time relative to the input size. Consequently, as the problem size increases, the time required to find an exact optimal solution may grow exponentially. Advancements in hardware are necessary to meet these increasing computing demands. However, the saturation of the physical boundaries of semiconductor miniaturization poses a significant obstacle to further reducing transistor sizes. Simple parallel processing is not power efficient and does not meet the exponentially growing demand in data processing speed and capacity. Therefore, a logical progression is shifting away from universal Turing Machines and exploring alternative computing methodologies for specific tasks.


Solver Type

Qudit - Constrained Discrete Number Optimization

Hardware Type

Hybrid Analog Machine with Quantum Optics and Digital Electronics

Maximum Number of Variables


Order of Correlation

Any types of first- through
fifth-order correlations,
where the interaction amongst variables
can be repulsive (positive correlation)
or attractive (negative correlation).



System Power Consumption

under 100 Watts

Storage Temperature

-25◦C to 85◦C

Operating Temperature

20◦C to 27◦C

Maximum Rate of Change

2◦C per hour

Software Requirement

On-prem: eqc-direct software package, Python 3.10.6 (recommended)

Cloud: qci-client software package

OS requirement

On-prem: Linux (recommended)


5U rack-mounted