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Finding treasures in an expanded design space
The term Design Space Exploration (DSE) is often used in the context of electronic design. As described in Wikipedia:
DSE refers to systematic analysis and pruning of unwanted design points based on parameters of interest. DSE is important given the complex specification of electronic systems and the plethora of design choices ranging from the choice of components, number of components, operating modes of each of the components, connections between the components, choice of algorithm, etc. The exploration process is complex because of a variety of ways in which the same functionality can be implemented. A tradeoff analysis between each of the implementation option based on a certain parameter of interest forms the basis of DSE.
Similar to electronic design, the design space when implementing quantum algorithms is also very large. Each functional block in a quantum algorithm can be implemented in numerous ways. For instance:
- A simple adder can be implemented as QFT adder or as a ripple adder
- Different gates can be used to achieve the same results
- Adding auxiliary qubits can reduce the depth of the block
- Auxiliary qubits could be reused for downstream blocks if they are appropriately uncomputed
- Commutative terms (such as in a Hamiltonian) can be sequenced in multiple ways
Given a multi-dimensional design space, not all design options are valid. Designers also face constraints. In electronic circuits these constraints might be cost, power consumption, regulatory limits or the physical size of the circuit. In quantum algorithm design, these constraints might be the number of qubits, the depth of the circuit, a preference to use certain gates, uneven noise characteristics and more.
Often, the 'best' design is subjective. Just like a C compiler can optimize for code size, speed or memory consumption, the optimal design depends on the preferences of the customer.
But as circuits become larger and more complex, the number of possible implementations grows exponentially. Without automated tools, a quantum software engineer can explore only a tiny fraction of the possible design implementations. Such manual exploration is likely to miss the opportunity to substantially optimize the design. In an era when quantum computers are limited in many ways, failing to find these 'design treasures' means failure to squeeze optimal performance out of a given computer. In many cases, it can be the difference between finding a workable solution to a quantum problem and failing to do so.
The Classiq quantum synthesis engine explores numerous options in the quantum design space. With it, you are very likely to outperform any manual process and reach your quantum goals months or years ahead of the competition.
See the video below for an illustration of such an automatic exploration.
mage credit for the thumbnail of this post: Gerlach, Joachim & Hardt, Wolfram & Eikerling, Heinz-josef & Rosenstiel, Wolfgang & Gregory, Brent. (1997). Transformation-based High Level Design Space Exploration.
The term Design Space Exploration (DSE) is often used in the context of electronic design. As described in Wikipedia:
DSE refers to systematic analysis and pruning of unwanted design points based on parameters of interest. DSE is important given the complex specification of electronic systems and the plethora of design choices ranging from the choice of components, number of components, operating modes of each of the components, connections between the components, choice of algorithm, etc. The exploration process is complex because of a variety of ways in which the same functionality can be implemented. A tradeoff analysis between each of the implementation option based on a certain parameter of interest forms the basis of DSE.
Similar to electronic design, the design space when implementing quantum algorithms is also very large. Each functional block in a quantum algorithm can be implemented in numerous ways. For instance:
- A simple adder can be implemented as QFT adder or as a ripple adder
- Different gates can be used to achieve the same results
- Adding auxiliary qubits can reduce the depth of the block
- Auxiliary qubits could be reused for downstream blocks if they are appropriately uncomputed
- Commutative terms (such as in a Hamiltonian) can be sequenced in multiple ways
Given a multi-dimensional design space, not all design options are valid. Designers also face constraints. In electronic circuits these constraints might be cost, power consumption, regulatory limits or the physical size of the circuit. In quantum algorithm design, these constraints might be the number of qubits, the depth of the circuit, a preference to use certain gates, uneven noise characteristics and more.
Often, the 'best' design is subjective. Just like a C compiler can optimize for code size, speed or memory consumption, the optimal design depends on the preferences of the customer.
But as circuits become larger and more complex, the number of possible implementations grows exponentially. Without automated tools, a quantum software engineer can explore only a tiny fraction of the possible design implementations. Such manual exploration is likely to miss the opportunity to substantially optimize the design. In an era when quantum computers are limited in many ways, failing to find these 'design treasures' means failure to squeeze optimal performance out of a given computer. In many cases, it can be the difference between finding a workable solution to a quantum problem and failing to do so.
The Classiq quantum synthesis engine explores numerous options in the quantum design space. With it, you are very likely to outperform any manual process and reach your quantum goals months or years ahead of the competition.
See the video below for an illustration of such an automatic exploration.
mage credit for the thumbnail of this post: Gerlach, Joachim & Hardt, Wolfram & Eikerling, Heinz-josef & Rosenstiel, Wolfgang & Gregory, Brent. (1997). Transformation-based High Level Design Space Exploration.
